Triple
T20453233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blush |
E501707
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Softly
Softly is a song featured on the album "Blush."
|
E1432347
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Softly | Statement: [Blush, hasPart, Softly]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Softly Context triple: [Blush, hasPart, Softly]
-
A.
Softly, Softly
Softly, Softly is a British police procedural television series from the 1960s that followed regional crime squads and became well known for its realistic depiction of police work.
-
B.
Softly and Tenderly
"Softly and Tenderly" is a classic Christian hymn, often sung as an invitation song, known for its gentle melody and themes of Jesus calling believers to come home.
-
C.
Tenderly
"Tenderly" is a popular jazz standard and romantic ballad that has been widely recorded by prominent jazz and pop artists.
-
D.
Love So Soft
"Love So Soft" is a soulful pop single by American singer Kelly Clarkson, known for its powerful vocals and retro-inspired production.
-
E.
Sweetie
Sweetie is a 1989 Australian black comedy-drama film directed by Jane Campion that explores a dysfunctional family through darkly surreal and psychologically intense storytelling.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Softly Triple: [Blush, hasPart, Softly]
Generated description
Softly is a song featured on the album "Blush."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Softly Target entity description: Softly is a song featured on the album "Blush."
-
A.
Softly, Softly
Softly, Softly is a British police procedural television series from the 1960s that followed regional crime squads and became well known for its realistic depiction of police work.
-
B.
Softly and Tenderly
"Softly and Tenderly" is a classic Christian hymn, often sung as an invitation song, known for its gentle melody and themes of Jesus calling believers to come home.
-
C.
Tenderly
"Tenderly" is a popular jazz standard and romantic ballad that has been widely recorded by prominent jazz and pop artists.
-
D.
Love So Soft
"Love So Soft" is a soulful pop single by American singer Kelly Clarkson, known for its powerful vocals and retro-inspired production.
-
E.
Sweetie
Sweetie is a 1989 Australian black comedy-drama film directed by Jane Campion that explores a dysfunctional family through darkly surreal and psychologically intense storytelling.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e0b4ac0a1c81908845d0f8a56abce8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e68d039af08190827bf765b50515a8 |
completed | April 20, 2026, 8:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a088b0e1d308190abb75555b9d20fd7 |
completed | May 16, 2026, 3:19 p.m. |
| NEDg | Description generation | batch_6a088be79cd0819092da701d6d3d2a8a |
completed | May 16, 2026, 3:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a088c7e46e48190b890f319ba4f2492 |
completed | May 16, 2026, 3:25 p.m. |
Created at: April 16, 2026, 11:32 a.m.